Speech emotion recognition based on data enhancement in time-frequency domain

Currently, there is a lack of voice samples in the speech emotion recognition field, which leads to poor recognition rate and over-fitting of data. Inspire by this, we propose speech emotion recognition based on data enhancement. The Berlin Emotional Corpus is enhanced from two directions: Time Domain and Frequency Domain. The samples was extracted and trained. Research and analyze the recognition rate of two classifiers: K-Nearest Neighbor and Support Vector Machine. Experiments show that the effect after data enhancement is better.